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Caseta Wireless smart lighting control and the Google Home Mini integrate seamlessly, giving you the ultimate convenience of voice controlled smart lighting. "Ok Google, turn on the living room lights" or "Hey Google, dim the kitchen lights 50%" are simple voice commands you can use to let the Google Assistant get your lights for you. With Caseta by Lutron and Google Home Mini, get the lights via the free Lutron App, from your voice, or right from the wall. Color: Charcoal. Material: Resin.

Caseta Wireless smart lighting control and the Google Home Mini integrate seamlessly, giving you the ultimate convenience of voice controlled smart lighting. "Ok Google, turn on the living room lights" or "Hey Google, dim the kitchen lights 50%" are simple voice commands you can use to let the Google Assistant get your lights for you. With Caseta by Lutron and Google Home Mini, get the lights via the free Lutron App, from your voice, or right from the wall. Color: Charcoal.

Neural networks are getting smaller. Much smaller. The OK Google team, for example, has run machine learning models that are just 14 kilobytes in size—small enough to work on the digital signal processor in an Android phone. With this practical book, you’ll learn about TensorFlow Lite for Microcontrollers, a miniscule machine learning library that allows you to run machine learning algorithms on tiny hardware.Authors Pete Warden and Daniel Situnayake explain how you can train models that are small enough to fit into any environment, including small embedded devices that can run for a year or more on a single coin cell battery. Ideal for software and hardware developers who want to build embedded devices using machine learning, this guide shows you how to create a TinyML project step-by-step. No machine learning or microcontroller experience is necessary.Learn practical machine learning applications on embedded devices, including simple uses such as speech recognition and gesture detectionTrain models such as speech, accelerometer, and image recognition, you can deploy on Arduino and other embedded platformsUnderstand how to work with Arduino and ultralow-power microcontrollersUse techniques for optimizing latency, energy usage, and model and binary size

Neural networks are getting smaller. Much smaller. The OK Google team, for example, has run machine learning models that are just 14 kilobytes in size—small enough to work on the digital signal processor in an Android phone. With this practical book, you’ll learn about TensorFlow Lite for Microcontrollers, a miniscule machine learning library that allows you to run machine learning algorithms on tiny hardware.Authors Pete Warden and Daniel Situnayake explain how you can train models that are small enough to fit into any environment, including small embedded devices that can run for a year or more on a single coin cell battery. Ideal for software and hardware developers who want to build embedded devices using machine learning, this guide shows you how to create a TinyML project step-by-step. No machine learning or microcontroller experience is necessary.Learn practical machine learning applications on embedded devices, including simple uses such as speech recognition and gesture detectionTrain models such as speech, accelerometer, and image recognition, you can deploy on Arduino and other embedded platformsUnderstand how to work with Arduino and ultralow-power microcontrollersUse techniques for optimizing latency, energy usage, and model and binary size